Exploring the Contribution of Isochrony-based Features to Computerized Assessment of Handwriting Disabilities

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Authors

GAVENČIAK Michal ZVONČÁK Vojtěch MEKYSKA Jiří ŠAFÁROVÁ Katarína ČUNEK Lukáš URBÁNEK Tomáš HAVIGEROVÁ Jana Marie BEDNÁŘOVÁ Jiřina GALÁŽ Zoltán MUCHA Ján

Year of publication 2022
Type Article in Proceedings
Conference 45th International Conference on Telecommunications and Signal Processing (TSP)
MU Faculty or unit

Faculty of Arts

Citation
Web https://ieeexplore.ieee.org/document/9851254
Doi http://dx.doi.org/10.1109/TSP55681.2022.9851254
Keywords Analytical models; Estimation error; Databases; Computational modeling; Machine learning; Signal processing; Predictive models
Description Approximately 30–60 % of the time children spend in school is associated with handwriting. However, up to 30 % of them experience handwriting disabilities (HD), which lead to a decrease in their academic performance. Current HD assessment methods are not unified and show signs of subjectivity which can lead to misdiagnosis. The aim of this paper is to propose a new approach to objective HD assessment based on the principle of movement isochrony. For this purpose, we used a database of 137 children attending a primary school, who performed a transcription and dictation task, and who were associated with a BHK (Concise Evaluation Scale for Children's Handwriting) score. Employing a machine learning model, we were able to estimate this score with 18 % error. An interpretation of the model suggests that the isochrony-based features could bring new benefits to the objective assessment of HD.
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